Timeline and episode-structured clinical data: pre-processing for Data Mining and analytics

Jing Lu; Alan Hales; David Rew; Malcolm Keech

doi:10.1109/ICDEW.2016.7495618

Timeline and episode-structured clinical data: pre-processing for Data Mining and analytics

Jing Lu
, Alan Hales
, David Rew
, Malcolm Keech

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Data Mining has been used in the healthcare domain for diagnosis and treatment analysis, resource management and fraud detection. It brings a set of tools and techniques that can be applied to large-scale patient data to discover underlying patterns and provide healthcare professionals an additional source of knowledge for making decisions. The Southampton Breast Cancer Data System (SBCDS) containing some 16,000 timeline-structured records is a visually rich and highly intuitive system for the manual and automated transfer of demographic, pathology and treatment data into an episode-based structure. While expansion of the data mining capability in SBCDS is one of the objectives of our research, real-world patient data is generally incomplete, inconsistent and containing errors. This case study will focus on the data pre-processing stage in order to clean the raw data and prepare the final dataset for use in data mining and analytics. Some initial results are given for sequential patterns mining and classification which highlight the advantages of the approach.

Original language	English
Title of host publication	nan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	64-67
ISBN (Print)	9781509021086
DOIs	https://doi.org/10.1109/ICDEW.2016.7495618
Publication status	Published - 23 Jun 2016
Event	IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) - Helsinki Duration: 16 May 2016 → 20 May 2016

Conference

Conference	IEEE 32nd International Conference on Data Engineering Workshops (ICDEW)
City	Helsinki
Period	16/05/16 → 20/05/16
Other	IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) (16/05/2016-20/05/2016, Helsinki)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Health informatics
Pre-Processing
breast cancer data
data mining
electronic patient records

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10.1109/ICDEW.2016.7495618

https://ieeexplore.ieee.org/document/7495618

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Cite this

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title = "Timeline and episode-structured clinical data: pre-processing for Data Mining and analytics",

abstract = "Data Mining has been used in the healthcare domain for diagnosis and treatment analysis, resource management and fraud detection. It brings a set of tools and techniques that can be applied to large-scale patient data to discover underlying patterns and provide healthcare professionals an additional source of knowledge for making decisions. The Southampton Breast Cancer Data System (SBCDS) containing some 16,000 timeline-structured records is a visually rich and highly intuitive system for the manual and automated transfer of demographic, pathology and treatment data into an episode-based structure. While expansion of the data mining capability in SBCDS is one of the objectives of our research, real-world patient data is generally incomplete, inconsistent and containing errors. This case study will focus on the data pre-processing stage in order to clean the raw data and prepare the final dataset for use in data mining and analytics. Some initial results are given for sequential patterns mining and classification which highlight the advantages of the approach.",

keywords = "Health informatics, Pre-Processing, breast cancer data, data mining, electronic patient records",

author = "Jing Lu and Alan Hales and David Rew and Malcolm Keech",

year = "2016",

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doi = "10.1109/ICDEW.2016.7495618",

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note = "IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) ; Conference date: 16-05-2016 Through 20-05-2016",

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T1 - Timeline and episode-structured clinical data: pre-processing for Data Mining and analytics

AU - Lu, Jing

AU - Hales, Alan

AU - Rew, David

AU - Keech, Malcolm

PY - 2016/6/23

Y1 - 2016/6/23

N2 - Data Mining has been used in the healthcare domain for diagnosis and treatment analysis, resource management and fraud detection. It brings a set of tools and techniques that can be applied to large-scale patient data to discover underlying patterns and provide healthcare professionals an additional source of knowledge for making decisions. The Southampton Breast Cancer Data System (SBCDS) containing some 16,000 timeline-structured records is a visually rich and highly intuitive system for the manual and automated transfer of demographic, pathology and treatment data into an episode-based structure. While expansion of the data mining capability in SBCDS is one of the objectives of our research, real-world patient data is generally incomplete, inconsistent and containing errors. This case study will focus on the data pre-processing stage in order to clean the raw data and prepare the final dataset for use in data mining and analytics. Some initial results are given for sequential patterns mining and classification which highlight the advantages of the approach.

AB - Data Mining has been used in the healthcare domain for diagnosis and treatment analysis, resource management and fraud detection. It brings a set of tools and techniques that can be applied to large-scale patient data to discover underlying patterns and provide healthcare professionals an additional source of knowledge for making decisions. The Southampton Breast Cancer Data System (SBCDS) containing some 16,000 timeline-structured records is a visually rich and highly intuitive system for the manual and automated transfer of demographic, pathology and treatment data into an episode-based structure. While expansion of the data mining capability in SBCDS is one of the objectives of our research, real-world patient data is generally incomplete, inconsistent and containing errors. This case study will focus on the data pre-processing stage in order to clean the raw data and prepare the final dataset for use in data mining and analytics. Some initial results are given for sequential patterns mining and classification which highlight the advantages of the approach.

KW - Health informatics

KW - Pre-Processing

KW - breast cancer data

KW - data mining

KW - electronic patient records

U2 - 10.1109/ICDEW.2016.7495618

DO - 10.1109/ICDEW.2016.7495618

M3 - Conference contribution

SN - 9781509021086

SP - 64

EP - 67

BT - nan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE 32nd International Conference on Data Engineering Workshops (ICDEW)

Y2 - 16 May 2016 through 20 May 2016

ER -

Timeline and episode-structured clinical data: pre-processing for Data Mining and analytics

Abstract

Conference

UN SDGs

Keywords

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